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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Identification and demonstration of roGFP2 as an environmental sensor for cryogenic correlative light and electron microscopy

Cryogenic correlative light and electron microscopy (cryo-CLEM) seeks to leverage orthogonal information present in two powerful imaging modalities. While recent advances in cryogenic electron microscopy (cryo-EM) allow for the visualization and identification of structures within cells at the nanometer scale, information regarding the cellular environment, such as pH, membrane potential, ionic strength, etc., which influences the observed structures remains absent. Fluorescence microscopy can potentially be used to reveal this information when specific labels, known as fluorescent biosensors, are used, but there has been minimal use of such biosensors in cryo-CLEM to date. Here we demonstrate the applicability of one such biosensor, the fluorescent protein roGFP2, for cryo-CLEM experiments. At room temperature, the ratio of roGFP2 emission brightness when excited at 425 nm or 488 nm is known to report on the local redox potential. When samples containing roGFP2 are rapidly cooled to 77 K in a manner compatible with cryo-EM, the ratio of excitation peaks remains a faithful indicator of the redox potential at the time of freezing. Using purified protein in different oxidizing/reducing environments, we generate a calibration curve which can be used to analyze in situ measurements. As a proof-of-principle demonstration, we investigate the oxidation/reduction state within vitrified Caulobacter crescentus cells. The polar organizing protein Z (PopZ) localizes to the polar regions of C. crescentus where it is known to form a distinct microdomain. Finally, by expressing an inducible roGFP2-PopZ fusion we visualize individual microdomains in the context of their redox environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Differential programming enabled functional imaging with Lorentz transmission electron microscopy

Abstract Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Resolution Imaging of Cellulose Organization in Cell Walls by Field Emission Scanning Electron Microscopy

Field emission scanning electron microscopy (FESEM) is a powerful tool for analyzing surface structures of biological and nonbiological samples. However, when it is used to study fine structures of nanometer-sized microfibrils of epidermal cell walls, one often encounters tremendous challenges to acquire clear and undistorted images because of two major issues: (1) Preparation of samples suitable for high resolution imaging; due to the delicateness of some plant materials, such as onion epidermal cell walls, many things can happen during sample processing, which subsequently result in damaged samples or introduce artifacts. (2) Difficulties to acquire clear images of samples which are electron-beam sensitive and prone to charging artifacts at magnifications over 100,000×. In this chapter we described detailed procedures for sample preparation and conditions for high-resolution FESEM imaging of onion epidermal cell walls. Lastly, the methods can be readily adapted for other wall materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. Here, we quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.

3D alignment↗
NASA NTRS1987

Structural characterization and gas reactions of small metal particles by high resolution in-situ TEM (Transmission Electron Microscopy) and TED (Transmission Electron Diffraction)

The detection and size analysis of small metal particles supported on amorphous substrates becomes increasingly difficult when the particle size approaches that of the phase contrast background structures of the support. An approach of digital image analysis, involving Fourier transformation of the original image, filtering, and image reconstruction was studied with respect to the likelihood of unambiguously detecting particles of less than 1 nm diameter on amorphous substrates from a single electron micrograph.

Heinemann, K.↗
DOE OSTI2021

Thermal Stability of Quasi-1D NbS 3 Nanoribbons and Their Transformation to 2D NbS 2 : Insights from in Situ Electron Microscopy and Spectroscopy

Here in situ electron microscopy imaging and spectroscopy enabled us to study the evolution of quasi-1D NbS 3 -IV nanoribbons with respect to morphology and chemical structure at temperatures between room temperature and 1000 °C. Scanning transmission electron microscopy (STEM) experiments included imaging in the secondary electron, (transmitted) bright field, and high-angle annular dark-field modes while operating in the low kV regime. The results showed that NbS 3 -IV samples transform dramatically from smooth nanoribbons into highly textured configurations featuring polyhedral divots and steps. Similar in situ heating experiments conducted with aberration-corrected STEM revealed that bilayers of NbS 3 -IV chains convert topotactically into aligned 2H-NbS 2 sheets upon loss of sulfur. Atomic resolution imaging, fast Fourier transform analysis, and electron energy loss spectroscopy confirmed these chemical changes, from which we propose an atomistic mechanism for the NbS 3 -IV → 2H-NbS 2 conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Stable support films for electron microscopy

This disclosure provides systems, methods, and apparatus related to arrangements including electron microscopy grids. In one aspect an arrangement includes an electron microscopy grid. The electron microscopy grid comprises a first surface and a second surface, with the first surface having a holey carbon film disposed thereon. A plurality of lipid molecules are disposed in a hole in the holey carbon film. Each lipid molecule of the plurality of lipid molecules has a hydrophilic head and a hydrophobic tail. A biotin-binding protein is attached to the hydrophilic heads of the plurality of lipid molecules. The biotin-binding protein is crystalline.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
NASA NTRS2001

In Situ Observation of Carbonaceous Material in the Matrices of CV and CM Carbonaceous Chondrites: Preliminary Results from Energy Filtered Transmission Electron Microscopy

Energy filtered transmission electron microscopy shows that organic matter can be detected in situ in the matrices of carbonaceous chondrites at a spatial resolution of at least 1 nm. In CM chondrites, carbon is often associated with sulfide particles. Additional information is contained in the original extended abstract.

Brearley, A. J.↗
DOE OSTI2022

Observation of formation and local structures of metal-organic layers via complementary electron microscopy techniques

Abstract Metal-organic layers (MOLs) are highly attractive for application in catalysis, separation, sensing and biomedicine, owing to their tunable framework structure. However, it is challenging to obtain comprehensive information about the formation and local structures of MOLs using standard electron microscopy methods due to serious damage under electron beam irradiation. Here, we investigate the growth processes and local structures of MOLs utilizing a combination of liquid-phase transmission electron microscopy, cryogenic electron microscopy and electron ptychography. Our results show a multistep formation process, where precursor clusters first form in solution, then they are complexed with ligands to form non-crystalline solids, followed by the arrangement of the cluster-ligand complex into crystalline sheets, with additional possible growth by the addition of clusters to surface edges. Moreover, high-resolution imaging allows us to identify missing clusters, dislocations, loop and flat surface terminations and ligand connectors in the MOLs. Our observations provide insights into controllable MOL crystal morphology, defect engineering, and surface modification, thus assisting novel MOL design and synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Raw electron microscopy images for "The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra"

This dataset contains the raw transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images used in the main figures of the paper “The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra (2026).” All the images were acquired at the Materials Research Laboratory, University of Illinois at Urbana-Champaign, by Qian Chen group. 1. We provide five subfolders, each named according to the corresponding figure numbers in the paper. 2. All files in the subfolders for Figures 1–3 and 5 are named as "Panel [letter]_*", where [letter] (e.g., a, b, c) represents the raw images used for the corresponding panels. 3. All files in the subfolder for Figure 4 correspond to panel f and show the configurations of patchy tetrahedra synthesized at varying concentrations of iodide and 2-naphthalenethiol. They are named "Experiment_[number]", where [number] represents the corresponding data points in the phase diagram. 4. In TEM images, the bright and dark regions indicate the polymer patches and nanoparticle cores, respectively. 5. In SEM images, the bright and dark regions indicate the nanoparticle cores and polymer patches, respectively. 6. Abbreviations in file names: HAADF-STEM (high-angle annular dark-field scanning transmission electron microscopy), PINEM (photon-induced near-field electron microscopy), and RCP/LCP (left-/right-handed circularly polarized).

chirality↗
DOE OSTI2022

Testing fast reactor fuels in a thermal reactor: Comparison of transmutation metallic fuel alloys behavior by scanning electron microscopy

To optimize nuclear waste repository performance, the destruction of minor actinide elements, particularly Np and Am, in a neutron fast spectrum reactor is possible by incorporating these elements into nuclear fuel. Evaluating the performance of minor actinide containing fuel is of paramount importance to enabling this technology. However, such a task is challenging without an available domestic fast spectrum test reactor. A comparison of fuel performance tested in an available domestic thermal reactor at the Idaho National Laboratory, the Advanced Test Reactor, and in a fast spectrum reactor in France (Phénix) is presented here in this study. This study evaluates the capability of using a cadmium shrouded test position to mimic the power profile along the fuel radius present in fast spectrum reactors so that thermally driven phenomenon (e.g., constituent redistribution) can be evaluated in a thermal reactor and determined to be prototypical of a fast reactor. Thus, optical microscopy and scanning electron microscopy has been performed on irradiated 35U-29Pu-4Am-2Np-30Zr fuel samples (where the number preceding the element is the weight percent concentration) from the two mentioned reactors that present similar irradiation temperatures and power conditions. The results indicate that fuel performance phenomena are reproducible in the two irradiation conditions. The redistribution of Zr occurred in the same manner for the two samples. Similar partitioning of U-Pu-Zr phases was observed, and the behavior of Am was similar in the analyzed specimens. Finally, the overall microstructure evolution seems not to be affected by minor actinides addition compared to expected behavior of conventional U-19Pu-10Zr ternary metal fuels for both specimens. Slight differences in fuel cladding chemical interaction were, however, observed. This difference is likely driven by difference in cladding composition rather than irradiation conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative phase retrieval and characterization of magnetic nanostructures via Lorentz (scanning) transmission electron microscopy

Magnetic materials phase reconstruction using Lorentz transmission electron microscopy (LTEM) measurements have traditionally been achieved using longstanding methods such as off-axis holography (OAH) fast-Fourier transform technique and the transport-of-intensity equation (TIE). The increase in access to processing power alongside the development of advanced algorithms have allowed for phase retrieval of nanoscale magnetic materials with greater efficacy and resolution. Specifically, reverse-mode automatic differentiation (RMAD) and the extended electron ptychography iterative engine (ePIE) are two recent developments of phase retrieval that can be applied to analyzing micro-to-nano- scale magnetic materials. This work evaluates phase retrieval using TIE, RMAD, and ePIE in simulations of Permalloy (Ni 80 Fe 20 ) nanoscale islands, or nanomagnets. Extending beyond simulations, we demonstrate total phase retrieval and image reconstructions of a NiFe nanowire using OAH and RMAD in LTEM and ePIE in Lorentz-mode-4D scanning transmission electron microscopy experiments and determine the saturation magnetization through corroborations with micromagnetic modeling. Finally, we demonstrate the efficacy of these methods in retrieving the total phase and highlight its use in characterizing and analyzing the proximity effect of the magnetic nanostructures.

Lorentz transmission electron microscopy↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗